Custom Predictive Analytics System with ML Models

We integrate predictive ML models into your operational processes using predictive analytics techniques. Our predictive analytics system combines ML models for churn prediction, demand forecasting, and risk analysis, all integrated with your CRM via a feature store and ML pipeline. We use SHAP values for interpretability and time series forecasting for demand. Within 8–12 weeks, you get a working prototype with 2–3 models; in 5–7 months, a full platform with predictive analytics, Feature Store, and automated monitoring. We reduce costs by 15–30% — in financial terms, that's 2–10 million rubles annually for a medium business. Typical project cost ranges from $50,000 to $200,000, with typical ROI between 300-500% in the first year. We'll assess your project in 1–2 days — contact us for a consultation.

With 5+ years of experience and 20+ successful projects, we guarantee 15-30% cost reduction. For example, for a retail client we reduced inventory costs by 25% within 3 months, and for a financial institution we cut fraud false positives by 60%.

How do we ensure centralized architecture and real-time accuracy?

Each model uses the same data: transactions, logs, IoT streams. A Feature Store (Feast/Hopsworks) becomes the single source of features. For example, 'number of visits in 7 days' is computed once and used in churn, LTV, and fraud models. This cuts development time by 40% — 1.67x faster than manual feature engineering — and eliminates feature inconsistencies. Without a Feature Store, each team recalculates identical features, leading to duplication and errors in production.

We automate model retraining on a schedule (weekly for volatile data, monthly for stable). Each run includes data quality checks via Great Expectations and an A/B test: the new model is compared against the champion version. If metrics drop, automatic rollback. We also monitor data drift (PSI > 0.25 triggers an alert) and concept drift (accuracy on a 30-day rolling window). As a result, classification accuracy stays above 85%, and regression MAE stays below 12%.

What predictive tasks and methods do we use?

We go deep into 2–3 tasks to ensure >85% accuracy rather than spreading thin across 10 superficial models.

Comparing Forecasting Methods:

Prophet wins in transparency but lags behind XGBoost on sparse data. LSTM excels on complex time series but is compute-intensive. In practice, we often combine: start with XGBoost, then fine-tune with LSTM if enough data.

Data Pipeline and System Architecture

Feature engineering includes aggregates over windows of 7, 30, 90, 365 days, RFM patterns, and embedding vectors for high-cardinality categorical features. Example: for churn prediction, we compute 'support tickets in 30 days', 'average ticket in 90 days', 'days since last purchase'. All features live in the Feature Store and update via sliding windows. The pipeline is orchestrated with Apache Airflow with versioned DAGs.

┌─────────────────────────────────────────────────────┐ │ Data Sources: ERP, CRM, IoT, Logs, External APIs │ └─────────────────────┬───────────────────────────────┘ │ ┌─────────────────────▼───────────────────────────────┐ │ Data Platform: Data Warehouse + Feature Store │ │ (Snowflake/BigQuery + Feast/Hopsworks) │ └─────────────────────┬───────────────────────────────┘ │ ┌─────────────────────▼───────────────────────────────┐ │ ML Platform: Training + Serving │ │ (MLflow + Ray + Seldon/BentoML) │ └─────────────────────┬───────────────────────────────┘ │ ┌─────────────────────▼───────────────────────────────┐ │ Activation Layer: CRM hooks, Alerts, Dashboards │ └─────────────────────────────────────────────────────┘ 

Model Interpretability and Integration

SHAP values are the standard for explainability. For each prediction, we output the top 5 drivers and compare against baseline. For example: 'Customer will churn with probability 0.85: support tickets dropped 60%, last purchase 45 days ago.' This allows the sales manager to take targeted action — not just 'notify', but offer a specific retention product. Without interpretability, business users don't trust the model — SHAP solves that.

Predictions are delivered to the decision point:

• Salesforce: predictive score in the contact card via API
• HubSpot: custom property with churn risk, automatic workflow triggers
• SAP S/4HANA: predictive equipment failure warnings
• Custom systems: REST API + webhooks for real-time forecasts

Alerts: when churn probability > 0.7 — automatic task for the account manager. When failure probability > 0.8 — notification in CMMS.

What's included in the work

• Data and business process audit, target metric definition
• Feature engineering and Feature Store development
• Model training and validation (PyTorch, XGBoost, Prophet)
• CRM/ERP integration via API
• Drift monitoring and automated retraining
• Model documentation and client team training
• 3 months of post-production support
• Access to model dashboards and APIs
• Weekly progress reports during development

Common Pitfalls and Timeline

Neglecting data quality: Garbage in, garbage out. Always validate data with Great Expectations before training.

Ignoring feature drift: Features that were predictive last month may not be today. Automate drift detection.

Lack of business interpretability: A black-box model is useless if stakeholders can't understand its decisions. Always provide SHAP explanations.

Overfitting on historical patterns: Use time-based cross-validation and monitor out-of-sample performance.

Timeline:

1. Assessment (1–2 days): We analyze your data sources and business goals, provide a feasibility estimate.
2. Prototyping (4–6 weeks): Build 2–3 models with basic features, deliver an MVP.
3. Full platform (5–7 months): Scale to Feature Store, integrate with CRM/ERP, implement monitoring.
4. Post-production (3 months): Handover, support, and iterative improvement.

Get a free preliminary assessment — contact us for a consultation. We'll calculate the economic impact in 2 days and prepare a commercial proposal.